The Visual Object Tracking VOT2013 challenge results

Matej Kristan a Roman Pflugfelder b Ales Leonardis c Jiri Matas d Fatih Porikli e

Luka Cehovin a Georg Nebehay b Gustavo Fernandez b Tomas Vojır d Adam Gatt f

Ahmad Khajenezhad g Ahmed Salahledin h Ali Soltani-Farani g Ali Zarezade g

Alfredo Petrosino i Anthony Milton j Behzad Bozorgtabar k Bo Li l

Chee Seng Chan m CherKeng Heng l Dale Ward j David Kearney j

Dorothy Monekosso n Hakki Can Karaimer o Hamid R. Rabiee g Jianke Zhu p

Jin Gao q Jingjing Xiao c Junge Zhang r Junliang Xing q Kaiqi Huang r

Karel Lebeda s Lijun Cao r Mario Edoardo Maresca i Mei Kuan Lim m

Mohamed ELHelw h Michael Felsberg t Paolo Remagnino u Richard Bowden s

Roland Goecke v Rustam Stolkin c Samantha YueYing Lim l Sara Maher h

Sebastien Poullot w Sebastien Wong f Shin’ichi Satoh x Weihua Chen r Weiming Hu q

Xiaoqin Zhang q Yang Li p ZhiHeng Niu l

Abstract

Visual tracking has attracted a significant attention inthe last few decades. The recent surge in the number ofpublications on tracking-related problems have made it al-most impossible to follow the developments in the field. One

aUniversity of Ljubljana, SloveniabAustrian Institute of Technology, AustriacUniversity of Birmingham, United KingdomdCzech Technical University in Prague, Czech RepubliceAustralian National UniversityfDSTO, Edinburgh, SA, AustraliagSharif University of Technology, Tehran, IranhCenter for Informatics Science, Nile University, Giza, EgyptiParthenope University of Naples, ItalyjUniversity of South Australia, Mawson Lakes, SA, AustraliakVision and Sensing, ESTeM, University of Canberra, AustralialPanasonic R&D Center, Singapore

mCISP, University of Malaya, MalaysianEng. Design and Math., University of West England, United KingdomoIzmir Institute of Technology, TurkeypCollege of Computer Science, Zhejiang University, ChinaqNLPR, Institute of Automation, CAS, Beijing, ChinarChinese Academy of Sciences, ChinasUniversity of Surrey, United KingdomtLinkoping University, SwedenuRobotic Vision Team, Kingston University, United KingdomvIHCC, CECS, Australian National University, AustraliawNII, JFLI, Hitotsubashi, JapanxNII, Hitotsubashi, Japan

of the reasons is that there is a lack of commonly acceptedannotated data-sets and standardized evaluation protocolsthat would allow objective comparison of different trackingmethods. To address this issue, the Visual Object Track-ing (VOT) workshop was organized in conjunction withICCV2013. Researchers from academia as well as indus-try were invited to participate in the first VOT2013 chal-lenge which aimed at single-object visual trackers that donot apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark datasetfor evaluation of single-object visual trackers as well asthe results obtained by the trackers competing in the chal-lenge. In contrast to related attempts in tracker benchmark-ing, the dataset is labeled per-frame by visual attributes thatindicate occlusion, illumination change, motion change,size change and camera motion, offering a more system-atic comparison of the trackers. Furthermore, we have de-signed an automated system for performing and evaluatingthe experiments. We present the evaluation protocol of theVOT2013 challenge and the results of a comparison of 27trackers on the benchmark dataset. The dataset, the evalu-ation tools and the tracker rankings are publicly availablefrom the challenge website1.

1http://votchallenge.net

2013 IEEE International Conference on Computer Vision Workshops

978-0-7695-5161-6/13 $31.00 © 2013 IEEE

DOI 10.1109/ICCVW.2013.20

98

2013 IEEE International Conference on Computer Vision Workshops

978-1-4799-3022-7/13 $31.00 © 2013 IEEE

DOI 10.1109/ICCVW.2013.20

98

1. IntroductionVisual tracking is a rapidly evolving field of computer

vision that has been increasingly attracting attention of the

vision community. One reason is that it offers many chal-

lenges as a scientific problem. Second, it is a part of many

higher-level problems of computer vision, such as motion

analysis, event detection and activity understanding. Fur-

thermore, the steady advance of HW/SW technology in

terms of computational power, form factor and price, opens

vast application potential for tracking algorithms. Applica-

tions include surveillance systems, transport, sports analyt-

ics, medical imaging, mobile robotics, film post-production

and human-computer interfaces.

In this paper, we focus on single-object trackers that

do not apply pre-learned models of the object appearance

(model-free), since they offer a particularly large applica-

tion domain. The activity in the field is reflected by the

abundance of new tracking algorithms presented and evalu-

ated in journals and at conferences, and summarized in the

many survey papers, e.g., [17, 35, 14, 22, 36, 52, 32]. A

review of recent high-profile conferences like ICCV, ECCV

and CVPR shows that the number of accepted tracking pa-

pers has been consistently high (40-50 annually). At the

ICCV2013 conference, for example, 38 papers with the

topic motion and tracking were published. The topic was

the third most popular if measured by the number of ac-

cepted papers.

Evaluation of new tracking algorithms, and their com-

parison to the state-of-the-art, depends on three essential

components: (1) a dataset, (2) an evaluation protocol, and

(3) performance evaluation measures. Indeed, much of the

advances in several computer vision fields, like object de-

tection, classification and segmentation [12], optical-flow

computation [3], can be attributed to a ubiquitous access to

standard datasets and evaluation protocols [43]. Despite the

efforts invested in proposing new trackers, the field suffers

from a lack of established methodology for objective com-

parison.

1.1. Related work

One of the most influential performance analysis efforts

for object tracking is PETS (Performance Evaluation of

Tracking and Surveillance) [53]. The first PETS work-

shop that took place in 2000, aimed at evaluation of visual

tracking algorithms for surveillance applications. How-

ever, its focus gradually shifted to high-level event inter-

pretation algorithms. Other frameworks and datasets have

been presented since, but these focussed on evaluation of

surveillance systems and event detection, e.g., CAVIAR2,

i-LIDS 3, ETISEO4, change detection [19], sports analytics

2http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA13http://www.homeoffice.gov.uk/science-research/hosdb/i-lids4http://www-sop.inria.fr/orion/ETISEO

(e.g., CVBASE5), or specialized on tracking specific objects

like faces, e.g. FERET [39] and [26].

Issues with datasets. A trend has emerged in the single-

object model-free tracking community to test newly pro-

posed trackers on larger datasets that include different real-

life visual phenomena like occlusion, clutter and illumi-

nation change. As a consequence, various authors nowa-

days compare their trackers on many publicly-available se-

quences, of which some have became a de-facto standard

in evaluation of new trackers. However, many of these se-

quences lack a standard ground truth labeling, which makes

comparison of proposed algorithms difficult. To sidestep

this issue, Wu et al. [48] have proposed a protocol for

tracker evaluation on a selected dataset that does not require

ground truth labels. However, this protocol is only appro-

priate for stochastic trackers. Furthermore, authors usually

do not use datasets with various visual phenomena equally

represented. In fact, many popular sequences exhibit the

same visual phenomenon, which makes the results biased

toward some particular types of the phenomena. In their

paper, Wu et al. [49] address this issue by annotating each

sequence with several visual attributes. For example, a se-

quence is annotated as “occlusion” if the target is occluded

anywhere in the sequence, etc. The results are reported only

on the subsets corresponding to a particular attribute. How-

ever, visual phenomena like occlusion do not usually last

throughout the entire sequence. For example, an occlusion

might occur at the end of the sequence, while the poor per-

formance is in fact due to some other effects occurring at

the beginning of the sequence. Thus a per-frame dataset

labeling is required to facilitate a more precise analysis.

Evaluation systems. For objective and rigorous eval-

uation, an evaluation system that performs on different

trackers the same experiment using the same dataset is

required. Most notable and general are the ODViS sys-

tem [23], VIVID [6] and ViPER [11] toolkits. The former

two focus on design of surveillance systems, while the latter

is a set of utilities/scripts for annotation and computation of

different types of performance measures. Recently, Wu et

al. [49] have performed a large-scale benchmark of several

trackers and developed an evaluation kit that allows integra-

tion of other trackers as well. However, in our experience,

the integration is not straightforward due to a lack of stan-

dardization of the input/output communication between the

tracker and the evaluation kit. Collecting the results from

the existing publications is an alternative to using an eval-

uation system that locally runs the experiment. However,

such evaluation is hindered by the biases the authors tend to

insert in their results. In particular, when publishing a paper

on a new tracker, a significant care is usually taken to adjust

the parameters of the proposed method such that it delivers

the best performance. On the other hand, much less atten-

5http://vision.fe.uni-lj.si/cvbase06/

9999

tion is given to competing trackers, leading to a biased pref-

erence in the results. Under the assumption that authors in-

troduce bias only for their proposed tracker, Pang et al. [38]

have proposed a page-rank-like approach to data-mine the

published results and compile unbiased ranked performance

lists. However, as the authors state in their paper, the pro-

posed protocol is not appropriate for creating ranks of the

recently published trackers due to the lack of sufficiently

many publications that would compare these trackers.

Performance measures. A wealth of performance mea-

sures have been proposed for single-object tracker evalu-

ation. These range from basic measures like center er-

ror [40], region overlap [31], tracking length [29] and fail-

ure rate [28, 27] to more sophisticated measures, such as

CoTPS [37], which combine several measures into a single

measure. A nice property of the combined measures is that

they provide a single score to rank the trackers. A downside

is that they offer little insight into the tracker performance.

In this respect the basic measures, or their simple deriva-

tives, are preferred as they usually offer a straight-forward

interpretation. While some authors choose several basic

measures to compare their trackers, the recent study [44]

has shown that many measures are correlated and do not

reflect different aspects of tracking performance. In this

respect, choosing a large number of measures may in fact

again bias results toward some particular aspects of track-

ing performance. Thus a better strategy is to apply few less

correlated measures and combine them via ranking lists,

similarly to what was done in the change detection chal-

lenge [19].

VOT2013. Recognizing the above issues, the Visual Ob-

ject Tracking (VOT2013) challenge and workshop was or-

ganized. The goal was to provide an evaluation platform

that goes beyond the current state-of-the-art. In particular,

we have compiled a labeled dataset collected from widely

used sequences showing a balanced set of various objects

and scenes. All the sequences are labeled per-frame with

different visual attributes to aid a less biased analysis of

the tracking results. We have created an evaluation kit in

Matlab/Octave that automatically performs three basic ex-

periments on a tracker using the provided dataset. A new

comparison protocol based on basic performance measures

is also proposed. A significant novelty of the proposed

evaluation protocol is that it explicitly addresses the statis-

tical significance of the results and addresses the equiva-

lence of trackers. A dedicated VOT2013 homepage http://votchallenge.net/ has been set up, from which

the dataset, the evaluation kit and the results are publicly

available. The authors of tracking algorithms have an op-

portunity to publish their source code at the VOT homepage

as well, thus pushing the field of visual tracking towards a

reproducible research.

In the following we first review the VOT2013 challenge

(Section 2), the dataset (Section 2.1), the performance mea-

sures (Section 2.2), the VOT2013 experiments (Section 2.3)

and the evaluation methodology (Section 2.4), respectively.

The analysis of the VOT2013 results is provided in Sec-

tion 3 and Section 4 concludes the paper.

2. The VOT2013 challengeThe VOT2013 challenge targets the case in which a user

manually initializes a tracker in the first image of a se-

quence. In case the tracker fails (e.g., drifts away from the

target), the user would reinitialize the tracker at the image of

failure. The tracker is therefore required to predict a single

bounding box of the target for each frame of the sequence.

A failure is automatically detected by comparing the pre-

dicted bounding box with the ground truth annotation, in

case of zero overlap, a failure is proclaimed.

The organisers of VOT2013 provide an evaluation kit

and a dataset for performing objective evaluation of the

trackers. The authors attending the challenge are required

to integrate their tracker into the VOT2013 evaluation

kit, which automatically performs a standardized experi-

ment. The results are analyzed by the VOT2013 evaluation

methodology. For more details on the participation, please

refer to the challenge page6.

For the sake of simplicity of the evaluation kit, the

trackers participating in the VOT2013 challenge have to

be causal and always provide a complete reinitialization

when initialized by the evaluation kit. Causality requires

the tracker to solely process the frames from the initializa-

tion up to the current frame without using any information

from the future frames. If a tracker fails at some point dur-

ing tracking, it is reinitialized by the evaluation kit. A com-

plete reinitialization at time-step t requires that any learned

information, like appearance and dynamics from the previ-

ous frames, should be discarded.

Participants are expected to submit a single set of re-

sults per tracker. Participants who have investigated several

trackers should submit a single result per tracker. Changes

in the parameters do not constitute a different tracker. The

tracker is required to run with fixed parameters on all exper-

iments. The tracking method itself is allowed to internally

change specific parameters, but these have to be set auto-

matically by the tracker, e.g., from the image size and the

initial size of the bounding box, and are not to be set by

detecting a specific test sequence and then selecting the pa-

rameters that were hand-tuned to this sequence.

2.1. The VOT2013 dataset

The VOT2013 dataset includes various real-life visual

phenomena, while containing a small number of sequences

to keep the time for performing the experiments reasonably

6http://votchallenge.net/participation.html

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low. We initially collected a large pool of sequences that

have been used by various authors in the tracking commu-

nity. Each frame of the sequence was labeled with several

attributes and a subset of 16 sequences was selected from

this pool such that the various visual phenomena like occlu-

sion and illumination changes, were still represented well

within the selection.

For most of the selected sequences, the per-frame bound-

ing boxes placed over the object of interest were already

available. Since the bounding boxes were annotated by var-

ious authors, it is difficult to specify a common rule that

guided the annotators. It appears that the bounding boxes

were placed such that large percentage of pixels within the

bounding box (at least > 60%) belonged to the target. In

most cases, this percentage is quite high since the upright

bounding box tightly fits the target. But in some cases,

(e.g., the gymnastics sequence) where an elongated target

is rotating significantly, the bounding box contains a larger

portion of the background at some frames as well. After

inspecting all the bounding box annotations, we have re-

annotated those sequences in which the original annotations

were poor.

To gain a better insight into the performance of trackers,

we have manually or semi-manually labeled each frame in

each selected sequence with five visual attributes that reflect

a particular challenge in appearance degradation: (i) occlu-

sion, (ii) illumination change, (iii) motion change, (iv) size

change, (v) camera motion. In case a particular frame did

not correspond to any of the five degradations, we denoted

it as (vi) non-degraded. Such labeling allows us to compare

the trackers only on the subsets of frames corresponding to

the same attribute. In the following we will use the term

attribute sequence to refer to a set of frames with the same

attribute pooled together from all sequences in the dataset.

2.2. The VOT2013 performance measures

There exists an abundance of performance measures in

the field of visual tracking (e.g., [48, 38, 19, 26, 49]).

The guideline for choosing the performance measures was

the interpretability of the measures while selecting as few

measures as possible to provide a clear comparison among

trackers. Based on the recent analysis of widely-used per-

formance measures [44] we have chosen two orthogonal

measures: (i) accuracy and (ii) robustness. The accu-

racy measures how well the bounding box predicted by the

tracker overlaps with the ground truth bounding box. On

the other hand, the robustness measures how many times

the tracker loses the target during tracking. The tracking

accuracy at time-step t is defined as the overlap between

the tracker predicted bounding box ATt and the ground truth

bounding box AGt

φt =AG

t ∩ATt

AGt ∪AT

t

. (1)

As we will see later, we repeat the experiments multiple

times, which results in multiple measurements of accuracy

per frame. For further processing, the multiple measure-

ments are averaged, yielding a single, average, accuracy per

frame. We can summarize the accuracies in a set of frames

by calculating the average of these over the valid frames.

Note that all frames are not valid for computation of the ac-

curacy measure. In fact, the overlaps in the frames right af-

ter the initialization are biased toward higher overlaps since

the (noise-free) initialization starts at maximum overlap and

it takes a few frames of the burn-in period for the perfor-

mance to become unbiased by the initialization. In a prelim-

inary study we have determined by a large-scale experiment

that the burn-in period is approximately ten frames. This

means that ten frames after initialization will be labeled as

invalid for accuracy computation.

The robustness was measured by the failure rate mea-

sure, which counts the number of times the tracker drifted

from the target and had to be reinitialized. A failure was

detected once the overlap measure (1) dropped to zero. It

is expected that if a tracker fails in a particular frame it will

likely fail again if it is initialized in the next frame. To re-

duce this immediate correlation, the tracker was initialized

five frames after the failure. Again, due to multiple repeti-

tions of the experiment we will have multiple measurements

of failure rate on a given sequence of frames. The average

of these yields an average robustness on a given sequence.

2.3. The VOT2013 experiments

The challenge included the following three experiments:

• Experiment 1: This experiment tested a tracker on all

sequences in the VOT2013 dataset by initializing it on

the ground truth bounding boxes.

• Experiment 2: This experiment performed Experi-

ment 1, but initialized with noisy bounding box. By

noisy bounding box, we mean a randomly perturbed

bounding box, where the perturbation is in order of ten

percent of the ground truth bounding box size.

• Experiment 3: This experiment performed the Experi-

ment 1 on all sequences with the color images changed

to grayscale.

In Experiment 2 there was a randomness in the initial-

ization of the trackers. The bounding boxes were randomly

perturbed in position and size by drawing perturbations uni-

formly from ±10% interval of the ground truth bounding

box size. Trackers that did not use the color information

were allowed to be run only on Experiment 3 and the same

results were assumed also for the Experiment 1. All the ex-

periments were automatically performed by the evaluation

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kit7. A tracker was run on each sequence 15 times to obtain

a better statistic on its performance.

2.4. The VOT2013 evaluation methodology

Our goal was to compare the performance of trackers in

each experiment of Section 2.3 on the six different attribute

sequences from Section 2.1 with respect to the two perfor-

mance measures from Section 2.2. Since we need to es-

tablish how well a tracker performs compared to the other

trackers, we have developed a ranking-based methodology

akin to [9, 12, 19]. In short, within a single experiment,

we rank the trackers separately for each performance mea-

sure on each attribute sequence. By averaging the ranks of

each tracker over the different attributes we obtain the rank-

ing with respect to a performance measure. Giving equal

weight to all performance measures, we obtain the final

ranking on a selected experiment by averaging the corre-

sponding two rankings.

Note that a group of trackers may perform equally well

on a given attribute sequence, in which case they should be

assigned an equal rank. In particular, after ranking trackers

on an attribute sequence, we calculate for each i-th tracker

its corrected rank as follows. We determine for each tracker,

indexed by i, a group of equivalent trackers, which contains

the i-th tracker as well as any tracker that performed equally

well as the selected tracker. The corrected rank of the i-thtracker is then calculated as the average of the ranks in the

group of equivalent trackers.

To determine the group of equivalent trackers, we require

an objective measure of equivalence on a given sequence. In

case of accuracy measure, a per-frame accuracy is available

for each tracker. One way to gauge equivalence in this case

is to apply a paired test to determine whether the difference

in accuracies is statistically significant. In case the differ-

ences are Gaussian distributed, the Student’s T-test, which is

often used in the aeronautic tracking research [4], is the ap-

propriate choice. However, in a preliminary study we have

observed that the accuracies in frames are not always Gaus-

sian distributed, which might render this test inappropriate.

As alternative, we apply the Wilcoxon Signed-Rank test as

in [9]. In case of robustness, we obtain several measure-

ments of number of times the tracker failed over the entire

sequence in different runs. However, these cannot be paired,

and we use the Wilcoxon Rank-Sum (also known as Mann-

Whitney U-test) instead to test the difference in the average

number of failures.

When establishing equivalence, we have to keep in mind

that statistical significance of performance differences does

not directly imply a practical difference [10]. One would

have to define a maximal difference in performance of two

trackers at which both trackers are said to perform practi-

cally equally well. However, since we could not find clear

7https://github.com/vicoslab/vot-toolkit

means to objectively define this difference, we reserve our

methodology only to testing the statistical significance of

the differences. Note, however, that if such a difference was

available, our Wilcoxon equivalence tests can readily apply

it.

3. The VOT analysisIn this section we analyze the results of the challenge.

We begin with a short overview of the trackers considered

in the challenge and then present and interpret the overall re-

sults. More detailed description of the evaluated trackers as

well as a detailed analysis can be found in the Appendix A

and the VOT2013 homepage8, respectively.

3.1. Description of trackers

We have received 19 entries from various authors in

the VOT2013 challenge. All of these have performed the

baseline Experiment 1, 17 have performed all three ex-

periments, and one performed only Experiment 1 and 3.

The VOT committee additionally performed all three ex-

periments with eight baseline trackers. For these the de-

fault parameters were selected, or, when not available, were

set to reasonable values. Thus a total of 27 trackers were

included in the VOT2013 challenge. In the following we

briefly overview the entries and provide the reference to an

original published paper. In cases where the method was not

officially published, we refer to the Appendix A instead.

We have received two entries that applied back-

ground adaptation and subtraction to localize the target,

MORP (Appendix A.18) and STMT (Appendix A.24). Two

trackers applied key-point features to localize the target,

Matrioska [34] and SCTT (Appendix A.23). Several ap-

proaches were applying global generative visual model

for target localization: the incremental subspace-based

IVT [40], the histogram-based mean-shift tracker MS [7]

and its improved version CCMS (Appendix A.4), two chan-

nel blurring approaches DFT [42] and the EDFT [13], two

adaptive multiple-feature-combination-based AIF [5] and

CactusFl (Appendix A.3), and a sparsity-based PJS-S (Ap-

pendix A.20). Many trackers were based on the discrimina-

tive global visual models. Among these were the multiple-

instance-learning-based tracker MIL [2], the STRUCK [20]

and its derivative PLT (Appendix A.21), the compressive

tracking based CT [55] and its derivative RDET [41], the

sparsity-based ORIA [50] and ASAM (Appendix A.2), and

the graph-embedding-based GSDT [15]. The competi-

tion entries included several part-based trackers as well.

Namely, the generalized Hough-transform-based HT [18],

the LGT [45] and its extension LGT++ [51], and the edge-

based LT-FLO [30]. Some trackers were utilizing optical

flow, e.g., FoT [46], while the TLD [24] combined the local

8http://votchallenge.net/

102102

Table 1. Ranking results. Highest ranking tracker is marked with red color, the second highest is marked with blue color, and the third

highest is marked with green color. Last row displays a joined ranking for all three experiments, which were also used to order the trackers.

The trackers that have been verified by the VOT committee are denoted by the asterisk (·)∗.

Experiment 1 Experiment 2 Experiment 3

RA RR R RA RR R RA RR R RΣ

PLT∗ 7.51 3.00 5.26 4.38 3.25 3.81 5.90 2.83 4.37 4.48FoT∗ 4.56 11.15 7.85 5.14 10.84 7.99 3.08 9.19 6.13 7.33EDFT∗ 9.14 11.04 10.09 8.14 13.61 10.88 6.52 10.66 8.59 9.85

LGT++∗ 15.73 4.25 9.99 13.36 4.14 8.75 15.46 7.34 11.40 10.05

LT-FLO 6.40 17.40 11.90 7.43 14.27 10.85 8.00 12.63 10.31 11.02

GSDT 11.87 11.99 11.93 10.78 12.56 11.67 9.49 9.72 9.60 11.07

SCTT 4.75 16.38 10.56 7.65 16.49 12.07 6.00 16.49 11.24 11.29

CCMS∗ 10.97 10.95 10.96 6.94 8.87 7.91 12.10 18.35 15.23 11.36

LGT∗ 17.83 5.42 11.62 15.38 5.20 10.29 18.63 7.21 12.92 11.61

Matrioska 10.62 12.40 11.51 10.59 14.38 12.48 9.07 13.03 11.05 11.68

AIF 7.44 14.77 11.11 9.17 15.25 12.21 6.60 18.64 12.62 11.98

Struck∗ 11.49 13.66 12.58 13.24 12.64 12.94 9.82 11.20 10.51 12.01

DFT 9.53 14.24 11.89 11.42 15.58 13.50 11.44 11.32 11.38 12.25

IVT∗ 10.72 15.20 12.96 11.36 15.24 13.30 9.17 14.01 11.59 12.62

ORIA∗ 12.19 16.05 14.12 14.00 15.92 14.96 10.56 13.26 11.91 13.66

PJS-S 12.98 16.93 14.96 13.50 14.84 14.17 11.19 14.05 12.62 13.92

TLD∗ 10.55 22.21 16.38 7.83 19.75 13.79 10.03 18.60 14.31 14.83

MIL∗ 19.97 14.35 17.16 18.46 13.01 15.74 15.32 11.17 13.24 15.38

RDET 22.25 12.22 17.23 19.75 10.97 15.36 17.69 9.97 13.83 15.48

HT∗ 20.62 13.27 16.95 19.29 12.61 15.95 20.04 12.90 16.47 16.46

CT∗ 22.83 13.86 18.35 21.58 12.93 17.26 18.92 12.68 15.80 17.13

Meanshift∗ 20.95 14.23 17.59 18.29 16.94 17.62 22.33 15.97 19.15 18.12

SwATrack 15.81 15.88 15.84 13.97 16.06 15.02 27.00 27.00 27.00 19.29

STMT 23.17 21.31 22.24 22.17 19.50 20.84 20.67 16.96 18.81 20.63

CACTuS-FL 25.39 19.67 22.53 24.17 15.46 19.82 22.92 18.33 20.62 20.99

ASAM 11.23 15.09 13.16 27.00 27.00 27.00 27.00 27.00 27.00 22.39

MORP 24.03 27.00 25.51 24.31 26.00 25.15 27.00 27.00 27.00 25.89

motion estimates with discriminative learning of patches for

object re-detection.

3.2. Results

The results are summarized in Table 1 and visualized by

the A-R rank plots inspired by the A-R score plots [44],

which show each tracker as a point in the joint accuracy-

robustness rank space. For more detailed rankings and plots

please see the VOT2013 results homepage. At the time of

writing this paper, the VOT committee was able to ver-

ify some of the submitted results by re-running parts of

the experiments using the binaries of the submitted track-

ers. The verified trackers are denoted by (·)∗ in Table 1.

Looking at the baseline results (Experiment 1), the track-

ers ranked lowest are MORP, CACTuS-FL and STMT. The

low performance of MORP and STMS is not surprising,

since they both apply adaptive/dynamic background sub-

traction, which tends to be less robust in situations with

non-static camera and/or the background. The CaCtus-FL

is a more sophisticated tracker, however, the tracker does

not work well for the objects that significantly move with

respect to the image frame. The top performing trackers on

the baseline are PLT, FoT, LGT++, EDFT and SCTT. The

PLT stands out as a single-scale detection-based tracker that

applies on-line sparse structural SVM to adaptively learn a

discriminative model from color, grayscale and grayscale

derivatives. The tracker does not apply a motion model and

does not adapt the size of the target. On the other hand,

the FoT, LGT++ and EDFT do apply a motion model. All

of these trackers, except for EDFT, can be thought of as

part-based models. In particular, the PLT applies a sparse

SVM, FoT is an array of Lucas-Kanade predictors that are

robustly combined to estimate the object motion, the visual

model in LGT++ is a weakly coupled constellation of parts

and SCTT uses a sparse regression for target localization.

Here, we can consider sparse methods as part-based meth-

ods with parts organized in a rigid grid. The target local-

ization in PLT, FoT and EDFT is deterministic, while the

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Figure 1. The accuracy-robustness ranking plots with respect to the three experiments. Tracker is better if it resides closer to the top-right

corner of the plot.

LGT++ and SCTT are stochastic trackers.

When considering the results averaged over all three ex-

periments, the top-ranked trackers are PLT and FoT, fol-

lowed by EDFT and LGT++. The A-R ranking plots in Fig-

ure 1 offer further insights into the performance of trackers.

We can see that, in all three experiments, the PLT yields

by far the largest robustness. In the baseline experiment,

the two trackers that fairly tightly follow the PLT are the

LGT++ and the original LGT. We can see that we have the

same situation in experiment with noise, which means that

these three trackers perform quite well even in noisy initial-

izations in terms of robustness. However, when considering

the accuracy, we can see that the top performing tracker on

the baseline is in fact FoT, tightly followed by SCTT and

a RANSAC-based edge tracker LT-FLO. In the experiment

with noise, the FoT tracker comes second best to PLT, sug-

gesting a bit lower resilience to noisy initializations. This

might speak of a reduced robustness of the local motion

combination algorithm in FoT in case of noisy initializa-

tions. Considering the color-less sequences in Experiment

3, the PLT remains the most robust, however, the FoT comes

on top when considering the accuracy.

Figure 2 shows the A-R ranking plots of the Experi-

ment 1 separately for each attribute. The top ranked track-

ers in the averaged ranks remain at the top also with re-

spect to each attribute, with two exceptions. When consid-

ering the size change, the best robustness is still achieved by

PLT, however, the trackers that yield best trade-off between

the robustness and accuracy are the LGT++ and the size-

adaptive mean shift tracker CCMS. When considering oc-

clusion, the PLT and STRUCK seem to share the first place

in the best trade-off.

In summary, the sparse discriminative tracker PLT seems

to address the robustness quite well, despite that it does not

adapt the target size, which reduces its accuracy when the

size of the tracked object is significantly changing. On the

other hand, the part-based trackers with a rigid part constel-

lation yield a better accuracy at reduced robustness. The

robustness is increased with part-based models that relax

the constellation, but this on average comes at a cost of sig-

nificant drop in accuracy.

Apart from the accuracy and robustness, the VOT evalu-

ation kit also measured the times required to perform a rep-

etition of each tracking run. From these measurements, we

have estimated the average tracking speed of each tracker

(Table 3). Care has to be taken when interpreting these re-

sults. The trackers were implemented in different program-

ming languages and run on different machines, with differ-

ent levels of code optimization. However, we believe that

these measurements still give a good estimate of the track-

ers practical complexity. The trackers that stand out are the

PLT and FoT, achieving speeds in range of 150 frames per

second (C++ implementations).

Table 2. Degradation difficulty for the six visual attributes: camera

motion (camera), illumination change (illum.), object size change

(size), object motion change (mot) and non-degraded (nondeg).

camera illum. occl. size mot. nondegAcc. 0.57 0.57 0.58 0.42 0.57 0.61

Fail. 1.58 0.56 0.66 0.93 0.85 0.00

Next we have ranked the individual types of visual degra-

dation according to the tracking difficulty they present to

the tested trackers. For each attribute sequence we have

computed the median over the average accuracies and fail-

ure rates across all the trackers. This median scores were

the basis for the attribute ranking. The ranking results com-

puted from Experiment 1 are presented in Table 2. These re-

sults confirm that the subsequences that do not contain any

specific degradation present little difficulty for the trackers

in general. Most trackers do not fail on such intervals and

achieve best average overlap. On the other hand, camera

motion is the hardest degradation in this respect. One way

to explain this is that most trackers focus primarily on ap-

pearance changes of the target and do not explicitly account

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AIFASAMCACTuS−FLCCMSCTDFTEDFTFoTHTIVTLGT++LGTLT−FLOGSDTMatrioskaMeanshiftMILMORPORIAPJS−SPLTRDETSCTTSTMTStruckSwATrackTLD

Figure 2. The accuracy-robustness ranking plots of Experiment 1 with respect to the six sequence attributes. The tracker is better if it

resides closer to the top-right corner of the plot.

for changing background. Note that camera motion does

not necessarily imply that the object is significantly chang-

ing position in the image frame. In terms of accuracy the

hardest degradation is the changes of object size. This is

reasonable as many trackers do not adapt in this respect and

sacrifice their accuracy for a more stable visual model that

is more accurate in situations where the size of the target

does not change. Occlusions and illumination changes are

apparently less difficult according to these results. Note,

however, that occlusion does pose a significant difficulty to

the trackers but the numbers do not indicate extreme diffi-

culty. This might be because the occlusions in our dataset

are short-term and partial at best.

4. Conclusion

In this paper we have reviewed the VOT2013 challenge

and its results. The VOT2013 contains an annotated dataset

comprising many of the widely used sequences. All the se-

quences have been labeled per-frame with attributes denot-

ing various visual phenomena to aid a more precise analysis

of the tracking results. We have implemented an evaluation

kit in Matlab/Octave that automatically performs three ba-

sic experiments on the tracker using the new dataset. A new

comparison protocol based on basic performance measures

was also proposed. We have created a publicly-available

repository and web page that will host the VOT2013 chal-

lenge (dataset, evaluation kit, tracking results, source code

and/or binaries if the authors choose so). The results of

VOT2013 indicate that a winner of the challenge according

to the average results is the PLT tracker (Appendix A.21).

However, the results also show that trackers tend to special-

ize either for robustness or accuracy. None of the track-

ers consistently outperformed the others by all measures at

all sequence attributes. It is impossible to conclusively say

what kind of tracking strategy works best in general, how-

ever, there is some evidence showing that robustness tends

to be better for the trackers that do not apply global models,

but rather split the visual models into parts.

The absence of homogenization of the single-tracking

performance evaluation makes it difficult to rigorously com-

pare trackers across publications and stands in the way of

faster development of the field. We expect that the homoge-

nization of performance evaluation will not happen without

involving a critical part of the community and without pro-

viding a platform for discussion. The VOT2013 challenge

and workshop was an attempt toward this goal. Our future

work will be focused on revising the evaluation kit, dataset

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Table 3. Performance, implementation and evaluation environment

characteristics.

FPS Implem. HardwarePLT 169.59 C++ Intel Xeon E5-16200

FoT 156.07 C++ Intel i7-3770

EDFT 12.82 Matlab Intel Xeon X5675

LGT++ 5.51 Matlab / C++ Intel i7-960

LT-FLO 4.10 Matlab / C++ Intel i7-2600

GSDT 1.66 Matlab Intel i7-2600

SCTT 1.40 Matlab Intel i5-760

CCMS 57.29 Matlab Intel i7-3770

LGT 2.25 Matlab / C++ AMD Opteron 6238

Matrioska 16.50 C++ Intel i7-920

AIF 30.64 C++ Intel i7-3770

Struck 3.46 C++ Intel Pentium 4

DFT 6.65 Matlab Intel Xeon X5675

IVT 5.03 Matlab AMD Opteron 6238

ORIA 1.94 Matlab Intel Pentium 4

PJS-S 1.18 Matlab / C++ Intel i7-3770K

TLD 10.61 Matlab Intel Xeon W3503

MIL 4.45 C++ AMD Opteron 6238

RDET 22.50 Matlab Intel i7-920

HT 4.03 C++ Intel i7-970

CT 9.15 Matlab / C++ Intel Pentium 4

Meanshift 8.76 Matlab Intel Xeon

SwATrack 2.31 C++ Intel i7

STMT 0.24 C++ Intel Xeon X7460

CACTuS-FL 0.72 Matlab Intel Xeon X5677

ASAM 0.93 Matlab Intel i5-2400

MORP 9.88 Matlab Intel i7

as well as challenges through the feedbacks gained from the

community.

AcknowledgementsThis work was supported in part by the following re-

search programs and projects: Slovenian research agency

research programs P2-0214, P2-0094, Slovenian research

agency projects J2-4284, J2-3607, J2-2221 and European

Union seventh framework programme under grant agree-

ment no 257906. Jiri Matas and Tomas Vojir were sup-

ported by CTU Project SGS13/142/OHK3/2T/13 and by

the Technology Agency of the Czech Republic project

TE01020415 (V3C – Visual Computing Competence Cen-

ter).

A. Submitted trackersIn this appendix we provide a short summary of all track-

ers that were considered in the VOT2013 competition.

A.1. Tracking with an Adaptive Integrated Feature(AIF)

Submitted by:

Weihua Chen weihua.chen@nlpr.ia.ac.cnLijun Cao ljcao@nlpr.ia.ac.cnJunge Zhang jgzhang@nlpr.ia.ac.cnKaiqi Huang kqhuang@nlpr.ia.ac.cn

AIF tackles the discriminative learning problem in low

resolution, lack of illumination and clutter by presenting an

adaptive multi-feature integration method in terms of fea-

ture invariance, which can evaluate the stability of features

in sequential frames. The adaptive integrated feature (AIF)

consists of several features with dynamic weights, which

describe the degree of invariance of each single feature. The

reader is referred to [5] for details.

A.2. Adaptive Sparse Appearance Model Tracker(ASAM)

Submitted by:B. Bozorgtabar behzad.bozorgtabar@canberra.edu.auRoland Goecke roland.goecke@ieee.org

ASAM accounts for drastic appearance changes by mod-

elling the object as a set of appearance models. An online

algorithm is used based on a discriminative and generative

sparse representation. A two-stage algorithm is used to ex-

ploit both the information of the example in the first frame

and successive observations obtained online.

A.3. Competitive Attentional Correlation Trackerusing Shape and Feature Learning (CACTuS-FL)

Submitted by:Sebastien Wong sebastien.wong@dsto.defence.gov.auAdam Gatt adam.gatt@dsto.defence.gov.auAnthony Milton Anthony.Milton@IEEE.orgDale Ward Dale.Ward@unisa.edu.auDavid Kearney david.kearney@unisa.edu.au

CACTuS-FL tackles model drift of the object by addi-

tionally tracking sources of clutter and then assigning ob-

servations to the tracks that best describe the observations.

This tracker augments the work described in [16].

A.4. Color Correspondences Mean-Shift (CCMS)

Submitted by:Tomas Vojir vojirt1@fel.cvut.czJir Matas matas@cmp.felk.cvut.cz

The Color Correspondences Mean-Shift tracker max-

imizes a likelihood ratio of similarity between the tar-

get model and the target candidate and the similarity be-

tween the target candidate and the background model for

each color (histogram bin) separately by a standard Mean-

Shift iteration (as proposed by Comaniciu et al. [7]). The

weighted mean of the correspondences is then used as a mo-

tion estimation. This process is iterative and runs for each

frame until it converges or until the maximum number of

iteration is reached.

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A.5. Compressive Tracking (CT)

Submitted by:VOT2013 Technical Committee

The CT tracker uses an appearance model based on fea-

tures extracted from the multi-scale image feature space

with data-independent basis. It employs non-adaptive ran-

dom projections that preserve the structure of the image

feature space of objects. A very sparse measurement ma-

trix is adopted to efficiently extract the features for the ap-

pearance model. Samples of foreground and background

are compressed using the same sparse measurement ma-

trix. The tracking task is formulated as a binary classi-

fication via a naive Bayes classifier with online update in

the compressed domain. The reader is referred to [55] for

details and to http://www4.comp.polyu.edu.hk/

˜cslzhang/CT/CT.htm for code.

A.6. Distribution Fields for Tracking (DFT)

Submitted by:Michael Felsberg michael.felsberg@liu.se

A common technique for gradient descent based track-

ers is to smooth the objective function by blurring the

image. However, blurring destroys image information,

which can cause the target to be lost. DFT intro-

duces a method for building an image descriptor using

distribution fields, a representation that allows smooth-

ing the objective function without destroying information

about pixel values. The reader is referred to [42] and

to http://people.cs.umass.edu/˜lsevilla/trackingDF.html for code.

A.7. Enhanced Distribution Fields for Tracking(EDFT)

Submitted by:Michael Felsberg michael.felsberg@liu.se

The EDFT is a novel variant of the DFT [42]. EDFT

derives an enhanced computational scheme by employing

the theoretic connection between averaged histograms and

channel representations. The reader is referred to [13] for

details.

A.8. Flock of Trackers (FoT)

Submitted by:Tomas Vojir vojirt1@fel.cvut.czJiri Matas matas@cmp.felk.cvut.cz

The Flock of Trackers (FoT) estimates the object mo-

tion from the transformation estimates of a number of local

trackers covering the object. The reader is referred to [46]

for details.

A.9. HoughTrack (HT)

Submitted by:

VOT2013 Technical CommitteeHoughTrack is a tracking-by-detection approach based

on the Generalized Hough-Transform. The idea of Hough-

Forests is extended to the online domain and the center

vote based detection and back-projection is coupled with a

rough segmentation based on graph-cuts. This is in con-

trast to standard online learning approaches, where typi-

cally bounding-box representations with fixed aspect ratios

are employed. The original authors claim that HoughTrack

provides a more accurate foreground/background separa-

tion and that it can handle highly non-rigid and articu-

lated objects. The reader is referred to [18] for details

and to http://lrs.icg.tugraz.at/research/houghtrack/ for code.

A.10. Incremental Learning for Robust VisualTracking (IVT)

Submitted by:VOT2013 Technical Committee

The idea of the IVT tracker is to incrementally learn a

low-dimensional subspace representation, adapting online

to changes in the appearance of the target. The model up-

date, based on incremental algorithms for principal com-

ponent analysis, includes two features: a method for cor-

rectly updating the sample mean, and a forgetting factor to

ensure less modelling power is expended fitting older ob-

servations. The reader is referred to [40] for details and to

http://www.cs.toronto.edu/˜dross/ivt/ for

code.

A.11. Local-Global Tracking (LGT)

Submitted by:Luka Cehovin luka.cehovin@fri.uni-lj.siMatej Kristan matej.kristan@fri.uni-lj.siAles Leonardis ales.leonardis@fri.uni-lj.si

The core element of LGT is a coupled-layer visual

model that combines the target global and local appear-

ance by interlacing two layers. By this coupled constraint

paradigm between the adaptation of the global and the local

layer, a more robust tracking through significant appearance

changes is achieved. The reader is referred to [45] for de-

tails.

A.12. An enhanced adaptive coupled-layer visualLGTracker++ (LGTracker++)

Submitted by:Jingjing Xiao shine636363@sina.comRustam Stolkin r.stolkin@cs.bham.ac.ukAles Leonardis ales.leonardis@fri.uni-lj.si

LGTracker++ improves the LGT tracker [45] in the cases

of environment clutter, significant scale changes, failures

due to occlusion and rapid disordered movement. Algo-

rithmically, the scale of the patches is adapted in addition

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to adapting the bounding box. marginal patch distributions

are used to solve patch drifting in environment clutter. a

memory is added and used to assist recovery from occlu-

sion. situations where the tracker may lose the target are

automatically detected, and a particle filter is substituted for

the Kalman filter to help recover the target. The reader is re-

ferred to [51] for details.

A.13. Long Term Featureless Object Tracker (LT-FLO)

Submitted by:Karel Lebeda k.lebeda@surrey.ac.ukRichard Bowden r.bowden@surrey.ac.ukJi Matas matas@cmp.felk.cvut.cz

LT-FLO is designed to track texture-less objects. It sig-

nificantly decreases reliance on texture by using edge-points

instead of point features. The tracker also has a mechanism

to detect disappearance of the object, based on the stabil-

ity of the gradient in the area of projected edge-points. The

reader is referred to [30] for details.

A.14. Graph Embedding Based Semi-SupervisedDiscriminative Tracker (GSDT)

Submitted by:Jin Gao jgao10@nlpr.ia.ac.cnJunliang Xing jlxing@nlpr.ia.ac.cnWeiming Hu wmhu@nlpr.ia.ac.cnXiaoqin Zhang xqzhang@nlpr.ia.ac.cn

GSDT is based on discriminative learning, where pos-

itive and negative samples are collected for graph embed-

ding. GSDT adopts graph construction based classifiers

without the assistance of learning object subspace gener-

atively as previous work did. The tracker also uses a new

graph structure to characterize the inter-class separability

and the intrinsic local geometrical structure of the samples.

The reader is referred to [15] for details.

A.15. Matrioska

Submitted by:Mario Edoardo Maresca mariomaresca@hotmail.itAlfredo Petrosino petrosino@uniparthenope.it

Matrioska decomposes tracking into two separate mod-

ules: detection and learning. The detection module can use

multiple keypoint-based methods (ORB, FREAK, BRISK,

SURF, etc.) inside a fallback model, to correctly local-

ize the object frame by frame exploiting the strengths of

each method. The learning module updates the object

model, with a growing and pruning approach, to account

for changes in its appearance and extracts negative samples

to further improve the detector performance. The reader is

referred to [34] for details.

A.16. Meanshift

Submitted by:VOT2013 Technical Committee

Meanshift uses a feature histogram-based target repre-

sentation that is regularized by spatial masking with an

isotropic kernel. The masking induces spatially-smooth

similarity functions suitable for gradient-based optimiza-

tion, hence, the target localization problem can be formu-

lated using the basin of attraction of the local maxima.

Meanshift employs a metric derived from the Bhattacharyya

coefficient as similarity measure, and use the mean shift

procedure to perform the optimization. The reader is re-

ferred to [7] for details.

A.17. MIL

Submitted by:VOT2013 Technical Committee

MIL is a tracking-by-detection approach. MIL uses

Multiple Instance Learning instead of traditional su-

pervised learning methods and shows improved robust-

ness to inaccuracies of the tracker and to incorrectly

labeled training samples. The reader is referred to

[2] for details and to http://vision.ucsd.edu/

˜bbabenko/project_miltrack.shtml for code.

A.18. Object Tracker using Adaptive BackgroundSubtraction and Kalman Filter (MORP)

Submitted by:Hakki Can Karaimer cankaraimer@iyte.edu.tr

MORP basically works in three major steps: (i) pixels

are assigned to foreground by taking the difference between

the next image frame and the current background. MORP

uses an effective global thresholding technique in this step.

The current background is computed by averaging image

frames at the beginning of the tracking process and after the

first ten frames (adaptive background subtraction) . (ii) fore-

ground pixel (blobs) are processed by morphological open-

ing with a disc whose diameter is six pixels, then a mor-

phological closing with a disc whose diameter is ten pixels.

Blobs whose area is less than eight by eight pixel are elimi-

nated. After this step, the biggest remaining blob is selected

as the blob to be tracked. (iii) according to the detected blob

position and velocity, the next position of the object is cal-

culated by using a Kalman filter.

A.19. Online Robust Image Alignment (ORIA)

Submitted by:VOT2013 Technical Committee

The ORIA tracker treats the tracking problem as the

problem of online aligning a newly arrived image to previ-

ously well-aligned images. The tracker treats the newly ar-

rived image, after alignment, as being linearly and sparsely

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reconstructed by the well-aligned ones. The task is accom-

plished by a sequence of convex optimization that mini-

mizes the L1 norm. After that, online basis updating is

pursued in two different ways: (1) a two-stage incremen-

tal alignment for joint registration of a large image dataset

which is known a prior, and (2) a greedy online alignment

of dynamically increasing image sequences, such as in the

tracking scenario. The reader is referred to [50] for details.

A.20. Patchwise Joint Sparse-SOMP (PJS-S)

Submitted by:Ali Zarezade zarezade@ce.sharif.eduHamid R. Rabiee rabiee@sharif.eduAli Soltani-Farani a soltani@ce.sharif.eduAhmad Khajenezhad khajenezhad@ce.sharif.edu

PJS-S models object appearance using a dictionary com-

posed of target patches from previous frames. In each

frame, the target is found from a set of candidates via a

likelihood measure that is proportional to the sum of the

reconstruction error of each candidate patch. The tracker

assumes slow changes of object appearance, hence target

and traget candidates are expected to to belong to the same

subspace. PJS-S imposes this intuition by using joint spar-

sity inducing norms, to enforce the target and previous best

candidates to have the same sparsity pattern. The reader is

referred to [54] for details.

A.21. Single scale pixel based LUT tracker (PLT)

Submitted by:Cher Keng Heng Hengcherkeng235@gmail.comSamantha Yue Ying Lim yueying53@gmail.comZhi Heng Niu niuzhiheng@gmail.comBo Li libohit@gmail.com

PLT runs a classifier at a fixed single scale for each test

image, to determine the top scoring bounding box which

is then the result of object detection. The classifier uses a

binary feature vector constructed from color, grayscale and

gradient information. To select a small set of discrimina-

tive features, an online sparse structural SVM [20] is used.

Since the object can be non-rigid and the bounding box

may be noisy, not all pixels in the bounding box belong to

the object. Hence, a probabilistic object-background seg-

mentation mask from color histograms is created and used

to weight the features during SVM training. The resulting

weighted and convex problem can be solved in three steps:

(i) compute the probability that a pixel belongs to the object

by using its color. (ii) solve the original non-sparse struc-

tural SVM and (iii) shrink the solution [21], i.e. features

with smallest values are discarded. Since the feature vec-

tor is binary, the linear classifier can be implemented as a

lookup table for fast speed.

A.22. Random Diverse Ensemble Tracker (RDET)

Submitted by:Ahmed Salahledin ahmed.salaheldin.hussein@gmail.comSara Maher m.a.elhelw@googlemail.comMohamed ELHelw s.m.elkerdawy@gmail.com

RDET proposes a novel real-time ensemble approach to

tracking by detection. It creates a diverse ensemble using

random projections to select strong and diverse sets of com-

pressed features. The reader is referred to [41] for details.

A.23. Structural Convolutional Treelets Tracker(SCTT)

Submitted by:Yang Li fliyang89@zju.edu.cnJianke Zhu jkzhug@zju.edu.cn

SCTT is a generative tracker, which is mainly inspired

by convolutional treelets keypoint matching algorithm [47].

SCTT employs a two-layer treelets [1] to extract the im-

age features from the input video frames. The proposed

two-layer structural framework is able to improve the rep-

resentation power of treelets by dividing image into smaller

pieces while reducing the feature dimensionality. Once im-

age features are extracted, LSST-distance [8] is calculated

and the patch with the smallest distance as the tracked target

is selected. Note that the reconstruction error is under the

Laplace distribution in LSST-distance, which is more robust

to partial occlusions. When SCTT finds the nearest patch

with LSST-distance in image, a similarity update threshold

is set. As treelets require fewer samples than PCA, only

those patches with high confidence are added into the up-

dating process. Thus, the proposed updating strategy is very

robust to the noises.

A.24. Spatio-temporal motion triangulation basedtracker (STMT)

Submitted by:Sebastien Poullot poullot.sebastien@free.frShin’ichi Satoh satoh@nii.ac.jp

STMT is based on a two layer process: camera motion

estimation then object motion estimation. The process flow

begins by registering two frames, yielding the camera mo-

tion. Successive image frames are aligned, candidate ob-

jects are obtained by frame differencing and association is

established either by the intersection of bounding boxes or

by employing a SIFT matching.

A.25. Struck

Submitted by:VOT2013 Technical Committee

Struck presents a framework for adaptive visual object

tracking based on structured output prediction. By ex-

plicitly allowing the output space to express the needs of

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the tracker, need for an intermediate classification step is

avoided. The method uses a kernelized structured out-

put support vector machine (SVM), which is learned on-

line to provide adaptive tracking. The reader is referred

to [20] for details and to http://www.samhare.net/research/struck/code for code.

A.26. An Adaptive Swarm Intelligence-basedTracker (SwATrack)

Submitted by:Mei Kuan Lim imeikuan@siswa.um.edu.myChee Seng Chan cs.chan@um.edu.myDorothy Monekosso dorothy.monekosso@uwe.ac.ukPaolo Remagnino p.remagnino@kingston.ac.uk

SwATrack deems tracking as an optimisation problem

and adapted the particle swarm optimisation (PSO) algo-

rithm as the motion estimator for target tracking. PSO

is a population based stochastic optimisation methodology,

which was inspired by the behavioural models of bird flock-

ing. The reader is referred to [33] for details.

A.27. TLD

Submitted by:VOT2013 Technical Committee

TLD explicitly decomposes the long-term tracking task

into tracking, learning, and detection. The detector localizes

all appearances that have been observed so far and corrects

the tracker if necessary. The learning estimates the detec-

tor errors and updates it to avoid these errors in the future.

The reader is referred to [25] for details and to https://github.com/zk00006/OpenTLD for code.

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[2] B. Babenko, M.-H. Yang, and S. Belongie. Robust object

tracking with online multiple instance learning. IEEE Trans.Pattern Anal. Mach. Intell., 33(8):1619–1632, 2011.

[3] S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black,

and S. R. A database and evaluation methodology for optical

flow. Int. J. Comput. Vision, 92(1):1–31, 2011.

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